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OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale

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arxiv 2503.02240 v2 pith:BRMIIGMF submitted 2025-03-04 cs.CL cs.DB

Haoyang Li , Shang Wu , Xiaokang Zhang , Xinmei Huang , Jing Zhang , Fuxin Jiang , Shuai Wang , Tieying Zhang
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Jianjun Chen Rui Shi Hong Chen Cuiping Li
This is my paper
classification cs.CLcs.DB
keywords text-to-sqldatadatasetslanguagellmsomnisqlavailableclosed-source
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL

    cs.CL 2026-07 conditional novelty 6.5 of 10

    An evolving Vulnerability Codex plus hypothesis-driven perturbations exposes latent Text-to-SQL failures in LLMs far better than fixed expert rules, with transferable patterns and early remediation gains.

  2. Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards

    cs.LG 2026-07 reject novelty 6.0 of 10

    Non-vacuous PAC-Bayes generalization bounds for billion-parameter RLVR models, obtained by a Gumbel-max reparameterization and aggressive TinyLoRA distillation/quantization, are claimed for four tasks.

  3. EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries

    cs.DB 2026-06 unverdicted novelty 6.0 of 10

    Query-driven table integration that uses Steiner-tree search to choose which joins LLMs must verify, reporting 30%+ accuracy gains at 5x lower LLM cost.

  4. PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning

    cs.AI 2025-09 reject novelty 5.0 of 10

    PaVeRL-SQL reports SOTA execution accuracy on Spider2.0-SQLite using partial-match rewards and verbal RL, but overclaims SOTA on Spider and BIRD.

  5. LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning

    cs.CL 2025-05 conditional novelty 5.0 of 10

    The authors built a 4,038-question text-to-SQL benchmark with chain-of-thought annotations and found that state-of-the-art models reach only 33.20% execution accuracy.

  6. CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation

    cs.AI 2025-07 reject novelty 3.0 of 10

    A 7B model trained with GRPO and a sparse execution-correctness reward reaches 59.97% execution accuracy on BIRD dev, though the evaluation protocol and baseline numbers contain inconsistencies.

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